[amp_mcq option1=”Lag” option2=”Random” option3=”Lead” option4=”None of the mentioned” correct=”option1″]<\/p>\n
The correct answer is A. Lag plot.<\/p>\n
A lag plot is a type of time series plot that shows the relationship between a variable and its lagged values. A lagged value is a value of the variable that is observed at a time point that is earlier than the current time point. Lag plots are often used to check for autocorrelation, which is a statistical phenomenon in which the values of a time series are correlated with their lagged values. Autocorrelation can be caused by a number of factors, including trend, seasonality, and serial correlation.<\/p>\n
To create a lag plot, you first need to calculate the lagged values of your variable. This can be done by subtracting the current time point from each value in your time series. Once you have calculated the lagged values, you can plot them against the current time points.<\/p>\n
A lag plot that shows no evidence of autocorrelation will have a random scatter of points. However, if there is autocorrelation, the points in the lag plot will tend to cluster together. The direction of the clustering will indicate the type of autocorrelation that is present. For example, if the points in the lag plot cluster upwards, then there is positive autocorrelation. If the points in the lag plot cluster downwards, then there is negative autocorrelation.<\/p>\n
Lag plots can be a useful tool for identifying autocorrelation in time series data. However, it is important to note that lag plots cannot be used to prove that autocorrelation is present. Only statistical tests can provide definitive evidence of autocorrelation.<\/p>\n
The other options are incorrect because they are not types of plots that are used to check if a data set or time series is random.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Lag” option2=”Random” option3=”Lead” option4=”None of the mentioned” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[737],"tags":[],"class_list":["post-51268","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\n